Intellectualization of the Monitoring Process of Street and Road Network Objects

The article substantiates the necessity of intellectualization of the monitoring process of street and road network facilities using modern mobile trail labs, which is caused by the high labor intensity and considerable length of monitoring objects, a large number of measurable parameters, and the need for rapid processing of large amounts of information. It is proposed to use data mining methods for intelligent data analysis and prediction of pavement strength, which allow solving problems of classification, regression, clustering, and prediction using artificial intelligence methods. The results of forecasting the longitudinal and lateral evenness, cracks, and road patches are presented. An original technology for a comprehensive assessment of the transport and operational condition of the road network based on developed modern high-tech equipment, which can be used for all types of inspections, is proposed. It is shown that the introduction of decision support systems for the intellectualization of the monitoring process of street and road network objects will make it possible to increase the technological efficiency of repair activities, analyze defects using a knowledge base, make a reasonable choice of road construction materials and technologies, and manage the repair work, which will allow the application of artificial intelligence methods in the promising area of decision-making on the repair of street and road networks, based on the results of diagnostics obtained by trail labs.